1. Field of the Invention
This invention relates generally to a system and method for estimating battery parameters and, more particularly, to a system and method for adaptively extracting six internal parameters from a lithium-ion battery to provide a reliable state-of-charge (SOC) estimate of the battery based on the battery's open circuit voltage.
2. Discussion of the Related Art
Electric vehicles are becoming more and more prevalent. These vehicles include hybrid vehicles, such as the extended range electric vehicles (EREV) that combines a battery and a main power source, such as an internal combustion engine, fuel cell system, etc., and electric only vehicles, such as the battery electric vehicles (BEV). All of these types of electric vehicles employ a high voltage battery that includes a number of battery cells. These batteries can be different battery types, such as lithium-ion, nickel metal hydride, lead acid, etc. A typical high voltage battery for an electric vehicle may include 196 battery cells providing about 400 volts. The battery can include individual battery modules where each battery module may include a certain number of battery cells, such as twelve cells. The individual battery cells may be electrically coupled in series, or a series of cells may be electrically coupled in parallel, where a number of cells in the module are connected in series and each module is electrically coupled to the other modules in parallel. Different vehicle designs include different battery designs that employ various trade-offs and advantages for a particular application.
Batteries play an important role in powering electrical vehicles and hybrid vehicles. The effectiveness of battery control and power management is essential to vehicle performance, fuel economy, battery life and passenger comfort. For battery control and power management, two states of the battery, namely, state-of-charge (SOC) and battery power, need to be predicted, or estimated, and monitored in real time because they are not measurable during vehicle operation. Battery state-of-charge and battery power can be estimated using an equivalent circuit model of the battery that defines the battery open circuit voltage (OCV), battery ohmic resistance and an RC pair including a resistance and a capacitance using the battery terminal voltage and current. Therefore, both battery states have to be derived from battery parameters estimated from the battery terminal voltage and current. A few battery state estimation algorithms have been developed in the art using different methodologies and some have been implemented in vehicles.
It is well known that battery dynamics are generally non-linear and highly dependent on battery operating conditions. However, for on-board battery parameter estimation, a linear model that has a few frequency modes is used to approximate a battery's dominant dynamics for a specific application, such as power prediction or SOC estimation. The reason for this is mainly due to limited computational power and memory available for on-board applications. In fact, even if there was unlimited computational power and memory, an accurate estimation of all battery parameters in a complex model with as many frequency modes as possible cannot be guaranteed because the extraction of signals, normally battery terminal voltage and terminal current, is limited. Therefore, it is neither practical nor necessary to cover all frequency modes in one model as long as the estimation error caused by model uncertainties is within an acceptable range for a specific application.
In order to minimize the memory and computational cost, a simple battery model is preferred. On the other hand, different applications need to be characterized by different frequency modes. For instance, the feature frequency to characterize the high frequency resistance of a battery is much higher than the feature frequency that characterizes a change in battery power. A simple model with limited frequency modes inevitably introduces errors and uncertainties because it cannot fully cover all feature frequencies for various applications.
U.S. patent application Ser. No. 11/867,497, filed Oct. 4, 2007, now published as Publication No. U.S. 2009/0091299, titled Dynamically Adaptive Method For Determining The State of Charge of a Battery, assigned to the assignee of this invention and herein incorporated by reference, discloses a method for determining battery state-of-charge and battery power using four battery parameters, namely, the battery OCV, ohmic resistance, and the resistance and capacitance of an RC pair.
Lithium-ion batteries have proven to be promising for hybrid electric vehicles. To better control the propulsion battery system in a hybrid electric vehicle for long battery life and good fuel economy, the knowledge of battery internal parameters, such as the OCV, the ohmic resistance, the battery capacitance, etc., is very important. In particular, the OCV is used to estimate the battery state-of-charge (SOC), which is a critical index for the control of batteries.
For some lithium-ion batteries, such as the nano-phosphate based lithium-ion battery, the existing parameter estimation algorithms have difficulty giving an accurate and robust SOC estimate because of a flat mapping curve from the OCV to the battery SOC. In these types of batteries, a chemical phenomenon known as diffusion, well known to those skilled in the art, causes the estimate of the open circuit voltage of the battery to be different from its actual open circuit voltage when no current is being drawn from the battery. For example, for every 10% SOC change, the OCV varies less than 20 mV. Thus, when no current is being drawn from the battery and the battery SOC is constant, the difference between the estimated open circuit voltage and the actual battery open circuit voltage is significant, which causes an error in the estimation of the battery SOC.
Therefore, it may be necessary to develop new estimation algorithms for certain batteries that can better estimate the open circuit voltage more precisely by subtracting all voltage components, including small components with slow dynamics, such as the diffusion voltage, which is considered to be small enough to be negligible for traditional lithium-ion batteries, from the battery terminal voltage. This requires a powerful algorithm to extract more battery parameters corresponding to more voltage components with high efficiency and accuracy. In consideration of the estimation error and measurement error with current in-vehicle sensors, the algorithm is also required to be highly robust to initial conditions, environment variations and measurement noise so as to provide a reliable SOC estimate.